Generally, the functionality of detection systems is defined by the capability to analyze a certain input sample (for example, a speech recording, a video or radar signal, etc.), compare it to a particular “claimed” or hypothesized pre-stored sample (e.g., a template or model) and to decide whether the observed test sample and the pre-stored sample match or not (i.e., to accept or reject the claim). The detection task can also be extended in a broader sense to cases involving a mixture of input samples, with the objective of detecting a particular claimed target within this mixture.
The quality of detection systems is measured primarily by evaluating two types of error (i.e., the expected values of such errors): “False Alarm Rate”, and “Miss Rate”. Low values of both measurements reflect more accurate systems. Typically, detection systems are trained/optimized according to criteria that minimize the two error rates simultaneously and along all operating points of the detection system. To such criteria belong maximum entropy, linear discriminative analysis, and indirectly, maximum likelihood.
To date, efforts towards such minimization have not yielded sufficiently desirable results. A need has therefore been recognized in connection with providing an arrangement that surpasses the performance hitherto encountered.